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international Joint conference on Neural Networks (IJCNN)

作者： Hu, Yaxin Barth, Erhardt Univ Lubeck Inst Neuro & Bioinformat Lubeck Germany Pattern Recognit Co GmbH Lubeck Germany Univ Lubeck Inst Neuro & Bioinformat Lubeck Germany

ISBN: (纸本)9798350359329;9798350359312

With the development of deep learning, video understanding has become a promising and challenging research field. In recent years, different transformer architectures have shown state-of-the-art performance on most benchmarks. Although transformers can process longer temporal sequences and therefor perform better than convolution networks, they require huge datasets and have high computational costs. The inputs to video transformers are usually clips sampled out of a video, and the length of the clips is limited by the available computing resources. In this paper, we introduce novel methods to sample and tokenize the input video, such as to better capture the dynamics of the input without a large increase in computational costs. Moreover, we introduce the MinBlocks as a novel architecture inspired by neural processing in biological vision. The combination of variable tubes and MinBlocks improves network performance by 10.67%.

关键词： Video Transformer Video Understanding Action recognition Variable Tubes MinBlocks UniFormerV2

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Proceedings of 2024 27th international conference on soft computing and Measurements, SCM 2024

Proceedings of 2024 27th International Conference on Soft Co...

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27th international conference on soft computing and Measurements, SCM 2024

ISBN: (纸本)9798350363708

The proceedings contain 109 papers. The topics discussed include: building a simulation model of a neonatal incubator with a neural controller;digital twin based socio-cyber-physical systems;measures for quantifying the rate of change in firm size in econometrics problems;speech emotion recognition using deep learning;network cybercrime analysis;threats to the resilience of cloud platforms;risk identification of wind energy development in Iraq;potential of GIS systems and remote sensing technologies for groundwater control in transboundary areas in China;determination of requirements for measuring performance of pulse magnetic field meter;and application of graph isomorphism analysis algorithms for scheme-topological verification of complex microelectronic objects.

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Multimodal Sentiment Analysis With Image-Text Correlation Modal 16

Multimodal Sentiment Analysis With Image-Text Correlation Mo...

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international conferences on Internet of Things (iThings) / Green computing and Communications (GreenCom) / Cyber, Physical and Social computing (CPSCom) / Smart Data, SmartData and Congress on Cybermatics (Cybermatics)

作者： Li, Yuxin Jiang, Shan Chaomurilige Minzu Univ China Minist Educ Key Lab Ethn Language Intelligent Anal & Secur Go Beijing 100081 Peoples R China Minzu Univ China Sch Informat Beijing 100081 Peoples R China

ISBN: (纸本)9798350309461

Multi-modality sentiment analysis in the Social Internet of Things (SIoT) is an emerging area of study. The integration of information from different modalities is the essence of multi-modal sentiment analysis tasks. In social networks, there is often a correspondence between the text and images within a comment. Users tend to use emotionally charged words to describe the subjects in the images. Therefore, exploring the interaction between text and images helps improve the accuracy of sentiment recognition. We propose an Image-Text Correlation(ITC) model for multimodal emotion analysis. ITC is a three-layered architecture consisting of a text layer, an image layer, and a fusion layer. The text layer employs a soft attention mechanism to transform word representations of the text section into sentence representations. In the image layer, we input the image into the VGG-16 network to obtain the image representation. In the fusion layer, we calculate the correlation between sentences and images. Based on this correlation, weights are assigned to the sentences, allowing us to construct new document vectors for classification. Experiments conducted on the Yelp dataset demonstrate the effectiveness of our modal for multimodal sentiment analysis. The dataset comprises comments with both images and text, labeled with ratings ranging from 1 to 5 stars (considered as five distinct sentiment categories).

关键词： multimodal emotion recognition soft attention information fusion Internet of Things (IoT) Social Internet of Things (SIoT)

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A Multimodal Approach for Emotion recognition in Conversations Using the MELD Dataset

A Multimodal Approach for Emotion Recognition in Conversatio...

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2025 Asia-Europe conference on Cybersecurity, Internet of Things and soft computing, CITSC 2025

作者： He, Jiarong Dundee International Institution Central South University Changsha China

ISBN: (纸本)9798331504205

Emotion recognition in conversational contexts is a fundamental task in affective computing, with significant implications for applications in empathetic dialogue systems, social robotics, and advanced media analysis. Given the complexity of emotional expression, effective emotion recognition systems must integrate multiple modalities, including linguistic, acoustic, and visual cues. This paper presents a multimodal framework for emotion recognition, evaluated on the Multimodal EmotionLines Dataset (MELD) dataset, which consists of multiparty dialogues containing textual, acoustic, and visual data. The proposed system leverages a pretrained Bidirectional Encoder Representations from Transformers (BERT) model for text encoding, Mel-frequency cepstral coefficients (MFCCs) for acoustic features, and Residual Network-50 (ResNet-50) for extracting visual embeddings. These modality-specific features are then fused through a concatenation mechanism and passed through a linear classifier to predict one of seven emotion categories: neutral, joy, sadness, anger, surprise, fear, and disgust. Experimental results demonstrate that the proposed approach achieves a test accuracy of 73.56%, a weighted F1-score of 0.7254, outperforming unimodal baselines. Analysis of the confusion matrix indicates that the model excels at classifying common emotions, such as neutral and joy, but struggles with more subtle emotions, such as fear and disgust, which are often misclassified. Further evaluations, including ablation studies and comparisons with existing methods, underscore the efficacy of multimodal fusion. Future research will explore the integration of temporal attention mechanisms, context modeling across dialogue turns, and improved feature extraction strategies for more precise emotion recognition. © 2025 IEEE.

关键词： Emotion recognition

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Outlier Detection based Model for Event pattern recognition in Vehicular Networks 20

Outlier Detection based Model for Event Pattern Recognition ...

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2024 international conference on Wireless and Mobile computing Networking and Communications

作者： Zaraket, Kawthar Bennis, Ismail Harb, Hassan Jaber, Ali Abouaissa, Abdelhafid Lebanese Univ Doctoral Sch Sci & Technol MERCEL Lab Beirut Lebanon Univ Haute Alsace IRIMAS 2 Rue Freres Lumiere Mulhouse France Amer Univ Middle East Coll Engn & Technol Kuwait Kuwait

ISBN: (纸本)9798350387452;9798350387445

Today, detecting outliers plays a crucial role in modern transportation systems, improving traffic management and road safety. This paper introduces a new outlier detection-based model for recognizing event patterns in Vehicular Ad hoc NETworks (VANETs). Our solution utilizes advanced techniques, combining outlier detection and multiclassification capabilities to enhance the resilience and reliability of transportation systems in dynamic and complex traffic scenarios. The approach involves three stages: data preprocessing and feature extraction, outlier detection, and *** the first stage, an image-based dataset is transformed into a feature-based dataset after essential preprocessing operations, such as feature extraction by analyzing local patterns in the image pixels using the Local Binary patterns (LBP) method. In the second stage, a hybrid classification model based on a neural network is proposed to identify outlier events in real-time vehicle data, followed by the employment of machine learning models to classify them as normal or abnormal. When an abnormal traffic situation is detected, the final stage utilizes multiclassification deep neural networks, specifically ResNet and Inception, to categorize events into predefined classes. Through extensive simulations using real VANET data, we have demonstrated the relevance and accuracy of our model in recognizing event patterns in traffic systems compared to other existing techniques.

关键词： VANET Outlier Detection Event pattern recognition Machine Learning Neural Network

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Recurrent and Mixture Density Architecture-based Driver pattern recognition 20

Recurrent and Mixture Density Architecture-based Driver Patt...

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2024 international conference on Wireless and Mobile computing Networking and Communications

作者： Zamouche, Djamila Ouazine, Kahina Benaissa, Mohamed Bouarouri, Djalal Aissani, Sofiane Univ Bejaia LIMED Lab Bejaia 06000 Algeria Estin Bejaia LIMED Lab Bejaia 06000 Algeria Univ Technol Troyes LIST3N Lab F-10000 Troyes France

ISBN: (纸本)9798350387452;9798350387445

Within the framework of Intelligent Transportation Systems (ITS), recognizing driving patterns is becoming of crucial importance, given the alarming rise in accident rates resulting from imprudent conduct behind the wheel. As vehicles become more advanced, the ability to identify and deal with risky behavior have become a key factor in improving road safety. In this work, we propose an approach that distinguishes between safe and unsafe driving, enabling self-control and adaptation of the driver's actions if necessary. The main idea of our solution is the use of LSTM-based Recurrent Neural Networks (RNN) combined with the Gaussian Mixture Method (GMM), which enables classification of driving styles into three distinct categories. The performance of the proposed approach has been evaluated using important evaluation metrics. The results show that the proposal offers encouraging performance, achieving an accuracy of over 93% for different driving styles.

关键词： Intelligent transportation systems Driver behavior recognition Safety RNN LSTM GMM

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Correction to: Automated facial expression recognition using exemplar hybrid deep feature generation technique (soft computing, (2023), 27, 13, (8721-8737), 10.1007/s00500-023-08230-9)

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soft computing 2024年 1-2页

作者： Baygin, Mehmet Tuncer, Ilknur Dogan, Sengul Barua, Prabal Datta Tuncer, Turker Cheong, Kang Hao Acharya, U. Rajendra Department of Computer Engineering Faculty of Engineering Ardahan University Ardahan Turkey Interior Ministry Elazig Turkey Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey University of Southern Queensland ToowoombaQLD4350 Australia Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia Science Mathematics and Technology Cluster Singapore University of Technology and Design Singapore487372 Singapore School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia

The author found that the below references were not included in the published article. Kanade T, Cohn JF, Tian Y (2000) Comprehensive database for facial expression analysis. In: Proc-4th IEEE int conf autom face gesture recognition, FG 2000, pp 46–53. Lucey P, Cohn JF, Kanade T et al (2010) The extended Cohn–Kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression. In: 2010 IEEE comput soc conf comput vis pattern recognit-work CVPRW 2010, pp 94–101. Pantic M, Valstar M, Rademaker R, Maat L (2005) Web-based database for facial expression analysis. In: IEEE int conf multimed expo, ICME 2005, pp 317–321. Li S, Deng W, Du J (2017) Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2852–2861 Yin L, Wei X, Sun Y et al (2006) A 3D facial expression database for facial behavior research. In: FGR 2006 proc 7th int conf autom face gesture recognit 2006, pp 211–216. Goodfellow IJ, Erhan D, Luc Carrier P et al (2015) Challenges in representation learning: a report on three machine learning contests. Neural Netw 64:59–63. Zhang Z, Luo P, Loy CC, Tang X (2018) From facial expression recognition to interpersonal relation prediction. Int J Comput Vis 126:550–569. Chen L-F, Yen Y-S (2007) Taiwanese facial expression image database. Brain Mapp Lab Inst Brain Sci Natl Yang-Ming Univ Taipei, Taiwan Lyons MJ, Kamachi M, Gyoba J (2020) Coding facial expressions with Gabor wavelets (IVC special issue). Lyons MJ (2021) "Excavating AI" re-excavated: debunking a fallacious account of the JAFFE Dataset. Lundqvist D, Flykt A, Ohman A (1998) The Karolinska directed emotional faces (KDEF). CD ROM from Dep Clin Neurosci Psychol Sect Karolinska Institutet 2–2 Zhao G, Huang X, Taini M et al (2011) Facial expression recognition from near-infrared videos. Image Vis Comput 29:607–619. Goldberger J, Hinton GE, Roweis S, Salakhutdinov R

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Multimodal Facial recognition Based Attendance Monitoring System 4

Multimodal Facial Recognition Based Attendance Monitoring Sy...

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4th international conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Harini, H. Shreya, C. Shivani, B. Tejaswini, P.U. Patil, Amarnath Bangalore India

ISBN: (纸本)9798331529635

This research study focuses on developing an automated attendance system using facial recognition technology. The system utilizes the Local Binary patterns (LBP) algorithm to accurately recognize student faces. By replacing traditional manual attendance methods, this system aims to improve efficiency, accuracy, and reduce the time required for attendance marking. The system automates the face recognition process, eliminating the need for manual verification and reducing the potential for human error. This research contributes to enhancing the efficiency and effectiveness of educational administration. © 2024 IEEE.

关键词： Local binary pattern

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Fused Local pattern (FLP): A Novel Local Descriptor in Pose Variations 1

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14th international conference on soft computing and pattern recognition, SoCPaR 2022, and the 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2022

作者： Karanwal, Shekhar UK Dehradun India

ISBN: (数字)9783031275241

ISBN: (纸本)9783031275234

In this paper a new local descriptor is launched for Face recognition (FR) named as Fused Local pattern (FLP). FLP feature size is characterized from MRELBP-NI, RD-LBP and 6 × 6 MB-LBP. MRELBP-NI and 6 × 6 MB-LBP captures microstructure and macrostructure information from the higher and lower patches and RD-LBP captures useful information from radial pixels. Taking merits of all three descriptors their feature size are integrated and called as FLP. Compression and matching is done by PCA and SVMs. For experiments evaluation ORL dataset used. Experiments proves the effectiveness of FLP descriptor than singly implemented ones. FLP also beats various literature work. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Compaction

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2024 Joint 13th international conference on soft computing and Intelligent Systems and 25th international Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

2024 Joint 13th International Conference on Soft Computing a...

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Joint 13th international conference on soft computing and Intelligent Systems and 25th international Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

ISBN: (纸本)9798350373332

The proceedings contain 222 papers. The topics discussed include: a twin dual-arm robot testbed to collect the human demonstration data for imitation learning;spatiotemporal comparison of brain activities between quadruped image recognition and recalling processes;self-organizing method for word embedding;emotional estimation method through fusion of temporal skin temperature and eye state changes;predicting models of per day inbound/outbound baggage at terminal 2 of Taoyuan international Airport during 2019-2023;mixed-reality simulator for analysis of interactions between humans and crawler cranes;a study on the effects of ICL and CoT prompts on the reward function generation of a small language model;and real-time support for solution search in human-based evolutionary computation.

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